On the Convergence Rate of Off-Policy Policy Optimization Methods with Density-Ratio Correction

• URL: http://arxiv.org/abs/2106.00993v1
• Date: Wed, 2 Jun 2021 07:26:29 GMT
• Title: On the Convergence Rate of Off-Policy Policy Optimization Methods with Density-Ratio Correction
• Authors: Jiawei Huang, Nan Jiang
• Abstract summary: We study the convergence properties of off-policy policy improvement algorithms with state-action density ratio correction. We present two strategies with finite-time convergence guarantees. We prove that O-SPIM converges to a stationary point with total complexity $O(epsilon-4)$, which matches the convergence rate of some recent actor-critic algorithms in the on-policy setting.
• Score: 28.548040329949387
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we study the convergence properties of off-policy policy improvement algorithms with state-action density ratio correction under function approximation setting, where the objective function is formulated as a max-max-min optimization problem. We characterize the bias of the learning objective and present two strategies with finite-time convergence guarantees. In our first strategy, we present algorithm P-SREDA with convergence rate $O(\epsilon^{-3})$, whose dependency on $\epsilon$ is optimal. In our second strategy, we propose a new off-policy actor-critic style algorithm named O-SPIM. We prove that O-SPIM converges to a stationary point with total complexity $O(\epsilon^{-4})$, which matches the convergence rate of some recent actor-critic algorithms in the on-policy setting.

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